activity
20172023
most citedSelf-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos

53 citations · 98 across the 8 of their papers we have counts for

collaborators

8 papers

eess.SY2023★ 6 cited

Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers

Andreas Doering, Marius Wiggert, Hanna Krasowski +3

Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with hi…

cs.CV2023

A Gated Attention Transformer for Multi-Person Pose Tracking

Andreas Doering, Juergen Gall

Multi-person pose tracking is an important element for many applications and requires to estimate the human poses of all persons in a video and to track them over time. The associa…

cs.CV2021★ 4 cited

Keypoint Message Passing for Video-based Person Re-Identification

Di Chen, Andreas Doering, Shanshan Zhang +3

Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Ex…

cs.CV2020

PoseTrackReID: Dataset Description

Andreas Doering, Di Chen, Shanshan Zhang +2

Current datasets for video-based person re-identification (re-ID) do not include structural knowledge in form of human pose annotations for the persons of interest. Nonetheless, po…

cs.CV2020★ 53 cited

Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos

Umer Rafi, Andreas Doering, Bastian Leibe +1

Video annotation is expensive and time consuming. Consequently, datasets for multi-person pose estimation and tracking are less diverse and have more sparse annotations compared to…

cs.CV2019★ 1 cited

Unifying Part Detection and Association for Recurrent Multi-Person Pose Estimation

Rania Briq, Andreas Doering, Juergen Gall

We propose a joint model of human joint detection and association for 2D multi-person pose estimation (MPPE). The approach unifies training of joint detection and association witho…